The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Modeling the Self-Assembly of Peptide Amphiphiles into Fibers Using Coarse-Grained Molecular Dynamics
Abstract
We have studied the self-assembly of peptide amphiphiles (PAs) into a cylindrical micelle fiber starting from a homogeneous mixture of PAs in water using coarse-grained molecular dynamics simulations. Nine independent 16 μs runs all show spontaneous fiber formation in which the PA molecules first form spherical micelles, and then micelles form a three-dimensional network via van der Waals interactions. As the hydrophobic core belonging to the different micelles merge, the three-dimensional network disappears and a fiber having a diameter of ∼80 Å appears. In agreement with atomistic simulation results, water molecules are excluded from the hydrophobic core and penetrate to ∼15 Å away from the axis of fiber. About 66% of the surface of fiber is covered with the IKVAV epitope, and ∼92% of the epitope is exposed to water molecules.
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Cites
Coarse Grained Model for Semiquantitative Lipid Simulations
Marrink, S. J., de Vries, A. H., & Mark, A. E. (2004) · The Journal of Physical Chemistry B · doi:10.1021/jp036508g · Cited by 2,257
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‘Improved parameters for the Martini coarse-grained protein force field’
de Jong, D. H., Singh, G., Bennett, W. F. D., Arnarez, C., Wassenaar, T. A., Schäfer, L. V., Periole, X., Tieleman, D. P., & Marrink, S. J. (2013) · Journal of Chemical Theory and Computation · doi:10.1021/ct300646g · Cited by 1,486
Two decades of Martini: Better beads, broader scope
Marrink, S. J., Monticelli, L., Melo, M. N., Alessandri, R., Tieleman, D. P., & Souza, P. C. T. (2023) · WIREs Computational Molecular Science · doi:10.1002/wcms.1620 · Cited by 255
Pathway Selection in Peptide Amphiphile Assembly
Korevaar, P. A., Newcomb, C. J., Meijer, E. W., & Stupp, S. I. (2014) · Journal of the American Chemical Society · doi:10.1021/ja503882s · Cited by 265
Design of self-assembly dipeptide hydrogels and machine learning via their chemical features
Li, F., Han, J., Cao, T., Lam, W., Fan, B., Tang, W., Chen, S., Fok, K. L., & Li, L. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1903376116 · Cited by 198
Accelerating the prediction and discovery of peptide hydrogels with human-in-the-loop
Xu, T., Wang, J., Zhao, S., Chen, D., Zhang, H., Fang, Y., Kong, N., Zhou, Z., Li, W., & Wang, H. (2023) · Nature Communications · doi:10.1038/s41467-023-39648-2 · Cited by 132
Metadata
- Authors
- Lee, O.-S., Cho, V., & Schatz, G. C.
- Venue
- Nano Letters
- Publisher
- American Chemical Society (ACS)
- Published
- 2012-08-31
- Volume
- 12
- Issue
- 9
- Pages
- 4907-4913
- ISSN
- 1530-6984 (ISSN-L)
- 1530-6992
- OpenAlex
- W2019926289
- Cited by
- 163
- References
- 1
- FWCI
- 4.21
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
